Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine
Background. Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID-19 based on machine learning with cloud computing act to assist radiologists a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/3111200 |
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| Summary: | Background. Cloud-based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID-19 based on machine learning with cloud computing act to assist radiologists and support telehealth services for remote diagnostics during this pandemic. Methods. In the proposed computer-aided diagnosis (CAD) system, the balance contrast enhancement technique (BCET) is utilized to enhance the chest X-ray images. Textural and shape-based features are extracted from the preprocessed X-ray images, and the fusion of these features generates the final feature vector. The gain ratio is applied for feature selection to remove insignificant features. An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID-19 detection. Results. However, to further improve the accuracy of ELM, we proposed bootstrap aggregated extreme learning machine (BA-ELM). The proposed cloud-based model is evaluated on a benchmark dataset COVID-Xray-5k dataset. We choose 504 (after data augmentation) and 100 images of COVID-19 for training and testing, respectively. Conclusion. Finally, 2000 and 1000 images are selected from the non-COVID-19 category for training and testing. The model achieved an average accuracy of 95.7%. |
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| ISSN: | 1607-887X |